Graph Classification
381 papers with code • 65 benchmarks • 46 datasets
Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.
( Image credit: Hierarchical Graph Pooling with Structure Learning )
Benchmarks
These leaderboards are used to track progress in Graph Classification
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Libraries
Use these libraries to find Graph Classification models and implementations
CIFAR-10
MNIST
Pubmed
OGB
Reddit
Cora
Latest papers
On the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social Networks
walidgeuttala/Synthetic-Benchmark-for-Graph-Classification
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The generalisation ability of these models is also analysed using a second synthetic network dataset (containing networks of different sizes). Our results point towards the balanced importance of the computational power of the GNN architecture and the the information level provided by the artificial features.
View-based Explanations for Graph Neural Networks
zju-daily/gvex
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Existing approaches aim to understand the overall results of GNNs rather than providing explanations for specific class labels of interest, and may return explanation structures that are hard to access, nor directly queryable. We propose GVEX, a novel paradigm that generates Graph Views for EXplanation.
On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
In this work, we assess attribution methods from a perspective not previously explored in the graph domain: retraining.
LightGCN: Evaluated and Enhanced
This paper analyses LightGCN in the context of graph recommendation algorithms.
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
nur-ag/elene
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We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats.
On the Initialization of Graph Neural Networks
lspongebobjh/virgo_icml2023
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In this paper, we analyze the variance of forward and backward propagation across GNN layers and show that the variance instability of GNN initializations comes from the combined effect of the activation function, hidden dimension, graph structure and message passing.
Hard Label Black Box Node Injection Attack on Graph Neural Networks
bryanzhou008/hard_label_black_box_attack_gnn
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In this work, we will propose a non-targeted Hard Label Black Box Node Injection Attack on Graph Neural Networks, which to the best of our knowledge, is the first of its kind.
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
pku-ml/laplaciancanonization
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NeurIPS 2023
However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graphs, sign and basis invariance, which also limits its effectiveness on graph data.
Mirage: Model-Agnostic Graph Distillation for Graph Classification
idea-iitd/mirage
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GNNs, like other deep learning models, are data and computation hungry.
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
anwar-said/esgea
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The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks.
